The determination of the underlying, an unknown state, or a temporal sequence of states, of a machine from noisy samples is a fundamental classification problem relevant to various machine diagnostics and data analytics applications. Herein, the term “machine” is used to generally to refer to any device that performs an intended action while being in various states over time. Example machines can include vehicles, electronic systems, medical machines, computer systems, entertainments devices, and the like.
A classification method addressing this problem takes as input data samples and outputs a reconstruction of the underlying hidden states or other relevant information regarding these states.
For example, the machine can be in one of two states, “normal” or “broken,” which cannot be directly observed. Instead, only noisy data, which are somehow related to the underlying states, can be obtained. Diagnosing whether the machine is functioning normally or is broken is a matter of inferring the underlying state from the acquired data. In general, there can be many states, e.g., “failure in component X,” “failure in component Y,” etc., and the machine can switch between the states over time.
One model that characterizes the situation of noisy data of an unknown temporally-evolving state is a hidden Markov model (HMM). Parameters of the HMM are the statistical distributions describing how the state evolves over time and how the samples are related to the underlying states. Given knowledge of these parameters, a Viterbi procedure, for example, is a classification method that outputs a most likely sequence of underlying states that produced the acquired data. Lacking knowledge of the parameters of the model can make the design of an effective classification method significantly more challenging.
So far, the above description of the problem involves a single party, e.g., a user of a client computer (client), which has access to the machine and can acquire the data, and directly applies the classification method to the data. However, the client may have insufficient computational resources.
Therefore, the invention consider a scenario that involves three parties, the client, a server computer (server) and a third-party computer (third-party) connected by a communication network, where the client acquires the data, and the third-party determines the underlying states, and the server provides assistance to enable the classification procedure for estimating the underlying states. The client wants the third-party to accurately determine the underlying states, perhaps motivated by other reasons, such as the desire to beneficially inform the third-party of the underlying machine behavior. For, example, the third-party may have the primary responsibility for maintaining the machine.
Other motivating factors for such a three-party scenario can also include asymmetries of information and/or computational capabilities between the client, server, and third-party, e.g., the server may have exclusive information about the machine model, better classification algorithms, and/or more computational resources, and external incentives for this scenario, i.e., the server and/or third-party provide a contracted maintenance service for the machine.
In the case of information asymmetry, it may be that neither the client, server, nor third-party alone has full knowledge of the machine parameters, i.e., the HMM statistical model, and thus, the coordination of these three entities may serve to produce a better reconstruction than any party could accomplish alone.
Naturally, there may be privacy constraints imposed by the client and the server in the context of this scenario. The client may wish to protect the privacy of the data by concealing the data to a reasonable degree, and/or the reconstructed states, e.g., to avoid revealing sensitive information related to the operation of the machine. The client may have different privacy requirements with respect to the server and the third-party.
For example, both the server and third-party may be service providers that are trusted to some degree, however the client still wishes to maintain as much privacy as possible while utilizing their services. The server may also wish to protect the privacy of its exclusive knowledge of the machine parameters by concealing the data to a reasonable degree, e.g., to maintain the value of its exclusivity. Thus, the problem is the construction of a coordinated classification method between these three parties that reconstructs of the underlying states while protecting the privacy of the involved parties.